World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
16080
World Ranking
12343
National Ranking
4996

Jasper Snoek publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Jasper Snoek sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 82 publications — 4th percentile

4% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Jasper Snoek D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Jasper Snoek sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Jasper Snoek is affiliated with Google in the United States and works primarily within the field of Computer Science. Their research output includes 78 publications, with a strong focus on Artificial Intelligence, which accounts for 62 of those works. Other subfields contributing to their research portfolio include Computer Vision and Pattern Recognition, Control and Systems Engineering, Computational Theory and Mathematics, and Management Science and Operations Research.

The scientist's research addresses various specialized topics related to machine learning and its applications. Major topics of study include:

  • Adversarial Robustness in Machine Learning
  • Gaussian Processes and Bayesian Inference
  • Machine Learning and Data Classification
  • Anomaly Detection Techniques and Applications
  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Fault Detection and Control Systems

Jasper Snoek has collaborated frequently with several researchers. Notable frequent coauthors include Balaji Lakshminarayanan, Rodolphe Jenatton, Zachary Nado, Dustin Tran, and Zelda Mariet.

The scientist has contributed to multiple publication venues with a particular emphasis on the arXiv repository, where 30 papers have been published. Other venues include Entropy and npj Digital Medicine.

Recent papers authored or coauthored by Jasper Snoek demonstrate ongoing engagement with topics in medical machine learning, robust model training, ensemble methods, and uncertainty quantification. Selected recent publications are:

  • "Second opinion needed: communicating uncertainty in medical machine learning," 2021, npj Digital Medicine
  • "Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift," 2020, arXiv (Cornell University)
  • "Plex: Towards Reliability using Pretrained Large Model Extensions," 2022, arXiv (Cornell University)
  • "A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness," 2022, arXiv (Cornell University)
  • "Hydra: Preserving Ensemble Diversity for Model Distillation," 2020, arXiv (Cornell University)

Best Publications

  • Practical Bayesian Optimization of Machine Learning Algorithms

    Jasper Snoek;Hugo Larochelle;Ryan P Adams

  • Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

    David R. Kelley;Jasper Snoek;John L. Rinn

  • Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift

    Yaniv Ovadia;Emily Fertig;Jie Ren;Zachary Nado

  • Scalable Bayesian Optimization Using Deep Neural Networks

    Jasper Snoek;Oren Rippel;Oren Rippel;Kevin Swersky;Ryan Kiros

  • Multi-Task Bayesian Optimization

    Kevin Swersky;Jasper Snoek;Ryan P Adams

  • Sequential regulatory activity prediction across chromosomes with convolutional neural networks

    David R. Kelley;Yakir A. Reshef;Maxwell Bileschi;David Belanger

  • Bayesian optimization with unknown constraints

    Michael A. Gelbart;Jasper Snoek;Ryan P. Adams

  • Likelihood Ratios for Out-of-Distribution Detection

    Jie Ren;Peter J. Liu;Emily Amanda Fertig;Jasper Roland Snoek

  • Second opinion needed: communicating uncertainty in medical machine learning

    Benjamin Kompa;Jasper Snoek;Andrew L. Beam

  • Spectral representations for convolutional neural networks

    Oren Rippel;Jasper Snoek;Ryan P. Adams

  • Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

    Carlos Riquelme;George Tucker;Jasper Roland Snoek

  • Input Warping for Bayesian Optimization of Non-Stationary Functions

    Jasper Snoek;Kevin Swersky;Rich Zemel;Ryan Adams

  • Freeze-Thaw Bayesian Optimization

    Kevin Swersky;Jasper Snoek;Ryan Prescott Adams

  • Learning Latent Permutations with Gumbel-Sinkhorn Networks

    Gonzalo E. Mena;David Belanger;Scott W. Linderman;Jasper Snoek

  • How Good is the Bayes Posterior in Deep Neural Networks Really

    Florian Wenzel;Kevin Roth;Bastiaan Veeling;Jakub Swiatkowski

  • Machine Learning Approaches in Cardiovascular Imaging.

    Mir Henglin;Gillian Stein;Pavel V. Hushcha;Jasper Snoek

  • Winner's Curse? On Pace, Progress, and Empirical Rigor.

    D. Sculley;Jasper Snoek;Alexander B. Wiltschko;Ali Rahimi

  • Hyperparameter Ensembles for Robustness and Uncertainty Quantification

    Florian Wenzel;Jasper Snoek;Dustin Tran;Rodolphe Jenatton

  • How Good is the Bayes Posterior in Deep Neural Networks Really

    Florian Wenzel;Kevin Roth;Bastiaan S. Veeling;Jakub Świątkowski

  • Plex: Towards Reliability using Pretrained Large Model Extensions

    Unknown

  • Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

    Zachary Nado;Shreyas Padhy;D. Sculley;Alexander D'Amour

Frequent Co-Authors

Ryan P. Adams
Ryan P. Adams Princeton University
Balaji Lakshminarayanan
Balaji Lakshminarayanan Google (United States)
Dustin Tran
Dustin Tran Google (United States)
Alex Mihailidis
Alex Mihailidis University of Toronto
Kevin Swersky
Kevin Swersky Google (United States)
Sebastian Nowozin
Sebastian Nowozin Microsoft (United States)
D. Sculley
D. Sculley Google (United States)
Hugo Larochelle
Hugo Larochelle Google (United States)
Richard S. Zemel
Richard S. Zemel University of Toronto
George Tucker
George Tucker Google (United States)

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